Content analysis is an important aspect for numerous applications, such as search engines, virus protection, advertising, data mining, and media analysis. The content that is analyzed can be in any form to begin with, but is often converted into written words before it is analyzed. The original source can be documents, broadcast programs, audio recordings, websites, email, or even live situations. While content analysis is performed on text associated with natural language (e.g., human language), natural language text often includes non-natural language (e.g., artificial language such as computer-executable language: C Language, C++, Java, JAVASCRIPT brand scripts, Structured Query Language (SQL), PYTHON brand scripts, Hypertext Processor (PHP), and the like) within the natural language text. For example, documents, E-mails, and websites (e.g., social media sites, chat rooms, and blogs) often include text that is artificial language, such as program code and program code fragments. The artificial language may be marked with a markup language tag, which makes it easy to identify and remove prior to performing content analysis on the natural language text. However, the artificial language may also appear in the form of plain text within the natural language text and thus often goes undetected and is therefore not identified or removed. As a result, during content analysis, the unidentified artificial language that has not been removed from within the natural language text is also analyzed, adding unwanted noise and/or inaccurate results to the content analysis.